【摘要】 研究无人机航路规划优化问题,为了提高无人机航路规划效率和精度,传统的遗传算法易陷入局部最优、收敛速度慢导致无人机航路规划效率低、寻优精度较差等问题。为解决上述问题,提出了一种基于改进遗传算法的无人机航路规划方法。改进算法前期采用了保优选择策略和改进编码方案对无人机航路进行优化,加快了搜索速度、提高规划效率,使之适应大规模威胁问题求解;后期结合无人机特点,改进交叉和变异算子,通过改进使得每轮搜索后每一软的最优航路能更好地反映求解的质量,有效地加快了收敛,保持了稳定性。最后用改进的遗传算法对无人机航路规划进行了仿真。实验结果表明,方法避免了陷入局部最优、收敛速度加快、寻优精度提高,并缩短了搜索时间,航路规划效率明显提高。提出的算法可以引申应用于类似情况下的路线规划问题,具有一定的推广意义。更多还原

【Abstract】 Research on route planning of unmanned aerial vehicle(uav).The problems to be solved include that the basic genetic algorithm may fall in local optimum,slow convergence speed leads low efficiency of route planning of unmanned aerial vehicle,and optimum is poor.In order to improve the accuracy and efficiency of uav route planning,this paper puts forward a modified genetic algorithm based on the uav route planning method.The improved algorithm adopted the optimal selection strategy and improvement of uavs coding scheme optimization route,speeding up the search speed,improving efficiency of planning,adapting problem and solving large-scale threats.Later,combining uavs improved crossover with mutation operators,through the improvement for each wheel search after each soft optimal route to better reflect the quality of solving,effectively avoid the local optimal,accelerate the convergence,and maintain the stability.Finally,the improved genetic algorithm for uav task route is simulated,and the results show that,compared with the basic genetic algorithm,this method can avoid being trapped in local optimum,convergence speed and accuracy are improved,and the optimal search time is shortened,which obviously improves the efficiency of route planning.The proposed algorithm can be applied in similar circumstances extension of route planning problem,and has certain significance for popularization.更多还原